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Exam Certified Generative AI Engineer Associate All Questions

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Exam Certified Generative AI Engineer Associate topic 1 question 22 discussion

Actual exam question from Databricks's Certified Generative AI Engineer Associate
Question #: 22
Topic #: 1
[All Certified Generative AI Engineer Associate Questions]

Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG’s response?

  • A. Assess the quality of the retrieved context
  • B. Implement caching for frequently asked questions
  • C. Use a different LLM to improve the generated response
  • D. Use a different semantic similarity search algorithm
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Suggested Answer: A 🗳️

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81a856e
6 days, 17 hours ago
Selected Answer: D
The issue described is that the RAG (Retrieval-Augmented Generation) application retrieves irrelevant context, which indicates that the retrieval mechanism is not accurately identifying the most relevant information. By using a different semantic similarity search algorithm, the engineer can improve the retrieval step to ensure that the most contextually relevant documents or data are selected before passing them to the LLM for generating responses. Options like A (assessing quality) may help diagnose the issue but do not directly fix it. B (caching) is not related to improving relevance, and C (using a different LLM) addresses generation quality, not retrieval accuracy.
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